The volumetric data set is important in many scientific and biomedical fields. Since such sets may be extremely large, a compression method is critical to store and transmit them. To achieve a high compression rate, most of the existing volume compression methods are lossy, which is usually unacceptable in biomedical applications. We developed a new context-based non-linear prediction method to preprocess the volume data set in order to effectively lower the prediction entropy. The prediction error is further encoded using Huffman code. Unlike the conventional methods, the volume is divided into cubical blocks to take advantage of the data’s spatial locality. Instead of building one Huffman tree for each block, we developed a novel binning algorithm that build a Huffman tree for each group (bin) of blocks. Combining all the effects above, we achieved an excellent compression rate compared to other lossless volume compression methods. In addition, an auxiliary data structure, Scalable Hyperspace File (SHSF) is used to index the huge volume so that we can obtain many other benefits including parallel construction, on-the-fly accessing of compressed data without global decompression, fast previewing, efficient background compressing, and scalability etc.